MetNet-2's remarkable performance makes it important to understand what the physics-free neural network is learning. This can help researchers gain new insight about interactions between different meteorological variables and ensure that the model conforms to our prior knowledge about weather physics. We adopt a state-of-the-art neural interpretation technique called Integrated Gradients to attribute predictions to the input variables [1]}. Among the notable findings, Figure REF  shows that the relative importance of absolute vorticity is small for near-term forecasts, but grows in importance as lead time increases all the way up to 12 hours. The importance of upper-level vorticity for a twelve hour forecast is consistent with what is known as quasi-geostrophic theory, a non-trivial set of simplifications and filtering of the equations of motion. A key result in the theory is that positive vorticity in the upper-troposphere is consistent with upward motion in the lower-troposphere [2]}. This upward motion does not directly trigger precipitation, but prepares the atmosphere for convection. See Supplement  for other key findings.
